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Chem Biol. 2014 Apr 24;21(4):541-51. doi: 10.1016/j.chembiol.2014.02.012. Epub 2014 Apr 3.

Large-scale identification and analysis of suppressive drug interactions.

Author information

1
Biological Sciences and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey; Nanotechnology Research and Application Center, Sabanci University, Istanbul 34956, Turkey. Electronic address: cokol@sabanciuniv.edu.
2
Biological Sciences and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey; Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.
3
Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.
4
Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA.
5
Department of Computational Biology, Boehringer Ingelheim Pharmaceuticals, Ridgefield, CT 06877, USA.
6
Biological Sciences and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey.
7
Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Pharmaceutical Sciences, University of British Columbia, 2405 Wesbrook Mall, Vancouver, BC V6T 1Z3, Canada.
8
Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.
9
Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Center for Cancer Systems Biology, Dana-Farber Cancer Institute, One Jimmy Fund Way, Boston, MA 02215, USA; Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON M5G 1X5, Canada; Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON M5S 3E1, Canada. Electronic address: fritz.roth@utoronto.ca.

Abstract

One drug may suppress the effects of another. Although knowledge of drug suppression is vital to avoid efficacy-reducing drug interactions or discover countermeasures for chemical toxins, drug-drug suppression relationships have not been systematically mapped. Here, we analyze the growth response of Saccharomyces cerevisiae to anti-fungal compound ("drug") pairs. Among 440 ordered drug pairs, we identified 94 suppressive drug interactions. Using only pairs not selected on the basis of their suppression behavior, we provide an estimate of the prevalence of suppressive interactions between anti-fungal compounds as 17%. Analysis of the drug suppression network suggested that Bromopyruvate is a frequently suppressive drug and Staurosporine is a frequently suppressed drug. We investigated potential explanations for suppressive drug interactions, including chemogenomic analysis, coaggregation, and pH effects, allowing us to explain the interaction tendencies of Bromopyruvate.

PMID:
24704506
PMCID:
PMC4281482
DOI:
10.1016/j.chembiol.2014.02.012
[Indexed for MEDLINE]
Free PMC Article
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